RGB'den gri tonlamaya

Bu egzersizde scikit-image modülündeki data içinden bir görsel yükleyip onu gri tonlamaya çevirecek ve çıktıda ikisini karşılaştıracaksın.

Matplotlib kullanarak görseli gösteren show_image(image, title='Image') fonksiyonunu önceden yükledik. Konsolda ?show_image() veya help(show_image) yazarak parametreleri hakkında daha fazla bilgi alabilirsin.

Rocket

Bu egzersiz

Python ile Görüntü İşleme

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Scikit-image'dan data ve color modüllerini içe aktar. İlk modül örnek görseller sağlar, ikincisi ise renk dönüştürme fonksiyonlarını içerir.
rocket görselini yükle.
RGB-3 roket görselini gri tonlamaya çevir.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import the modules from skimage
from skimage import ____, ____

# Load the rocket image
rocket = data.____()

# Convert the image to grayscale
gray_scaled_rocket = color.____(____)

# Show the original image
show_image(rocket, 'Original RGB image')

# Show the grayscale image
show_image(gray_scaled_rocket, 'Grayscale image')

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Görüntü İşleme

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

Jump into digital image structures and learn to process them! Extract data, transform and analyze images using NumPy and Scikit-image. With just a few lines of code, you will convert RGB images to grayscale, get data from them, obtain histograms containing very useful information, and separate objects from the background!

Exercise 1: scikit-image ile görüntülere hayat ver Exercise 2: Bu gri mi yoksa rengârenk mi?Exercise 3: RGB'den gri tonlamaya

Geçerli Egzersiz

Exercise 4: Görüntüler için NumPy Exercise 5: Ters düz çevirme Exercise 6: Histogramlar Exercise 7: Eşikleme ile başlarken Exercise 8: Küresel eşikleme uygula Exercise 9: Arka plan bu kadar bariz olmadığında Exercise 10: Diğer yöntemleri denemek Exercise 11: Eşikleme uygula

You will learn to <strong>detect object shapes</strong> using edge detection filters, <strong>improve medical images</strong> with contrast enhancement <strong>and even enlarge pictures to five times its original size! </strong> You will also apply morphology to make thresholding more accurate when segmenting images and go to the next level of processing images with Python.

Exercise 1: Jump into filtering Exercise 2: Edge detection Exercise 3: Blurring to reduce noise Exercise 4: Contrast enhancement Exercise 5: What's the contrast of this image?Exercise 6: Medical images Exercise 7: Aerial image Exercise 8: Let's add some impact and contrast Exercise 9: Transformations Exercise 10: Aliasing, rotating and rescaling Exercise 11: Enlarging images Exercise 12: Proportionally resizing Exercise 13: Morphology Exercise 14: Handwritten letters Exercise 15: Improving thresholded image

So far, you have done some very cool things with your image processing skills! In this chapter, you will apply <strong>image restoration to remove objects, logos, text, or damaged areas</strong> in pictures! You will also learn how to apply noise, use segmentation to speed up processing, and find elements in images by their contours.

Exercise 1: Image restoration Exercise 2: Let's restore a damaged image Exercise 3: Removing logos Exercise 4: Noise Exercise 5: Let's make some noise!Exercise 6: Reducing noise Exercise 7: Reducing noise while preserving edges Exercise 8: Superpixels & segmentation Exercise 9: Number of pixels Exercise 10: Superpixel segmentation Exercise 11: Finding contours Exercise 12: Contouring shapes Exercise 13: Find contours of an image that is not binary Exercise 14: Count the dots in a dice's image

After completing this chapter, you will have a deeper knowledge of image processing as you will be able to <strong>detect edges, corners, and even faces! </strong> You will learn how to detect not just front faces but also face profiles, cat, or dogs. You will apply your skills to more complex <strong>real-world applications.</strong> Learn to master several widely used image processing techniques with very few lines of code!

Exercise 1: Finding the edges with Canny Exercise 2: Edges Exercise 3: Less edgy Exercise 4: Right around the corner Exercise 5: Perspective Exercise 6: Less corners Exercise 7: Face detection Exercise 8: Is someone there?Exercise 9: Multiple faces Exercise 10: Segmentation and face detection Exercise 11: Real-world applications Exercise 12: Privacy protection Exercise 13: Help Sally restore her graduation photo Exercise 14: Amazing work!